An Enhanced Spectral Clustering Algorithm with S-Distance Kumar Sharma, Krishna Seal, Ayan Herrera Viedma, Enrique Krejcar, Ondrej S-divergence S-distance Spectral clustering This work is partially supported by the project "Prediction of diseases through computer assisted diagnosis system using images captured by minimally-invasive and non-invasive modalities", Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur India (under ID: SPARCMHRD-231). This work is also partially supported by the project "Smart Solutions in Ubiquitous Computing Environments", Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2204/2021); project at Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876 and the Fundamental Research Grant Scheme (FRGS) Vot5F073 supported by the Ministry of Education Malaysia for the completion of the research. Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms-k-means, density-based spatial clustering of applications with noise and conventional SC-are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon's signed-rank test, Wilcoxon's rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape. 2021-05-25T07:34:46Z 2021-05-25T07:34:46Z 2021-04-02 info:eu-repo/semantics/article Kumar Sharma, K.; Seal, A.; Herrera-Viedma, E.; Krejcar, O. An Enhanced Spectral Clustering Algorithm with S-Distance. Symmetry 2021, 13, 596. [https://doi.org/10.3390/sym13040596] http://hdl.handle.net/10481/68697 10.3390/sym13040596 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess AtribuciĆ³n 3.0 EspaƱa MDPI